Shifts in rainfall and rising temperatures due to climate change poses a formidable challenge to the sustainability of broadacre crop yields in Western and SouthEastern Australia. Output from18 Global Climate Models (GCMs) for the Special Report on Emission Scenarios (SRES) A2 scenario was statistically downscaled to four contrasting locations. For the first time in these regions, bias corrected statistically downscaled climate data were employed to drive the Agricultural Production Systems Simulator (APSIM) crop model that integrates the effects of soil, crop phenotype, and management options for a quantitative comparison of crop yields and phenology under an historical and a plausible projected climate. The dynamic APSIM simulation model explore the implications of climate change across multiple locations and multiple time periods (1961-2010, 2030, 2060 and 2090) for multiple key crops (wheat, barley, lupin, canola, field pea) grown in three different types of soil. On average, the ensemble of downscaled GCM projections show a decrease in rainfall in the future at the four locations considered, with increased variability at two locations. At all locations and for five crops, future changes in both crop biomass and grain yield are strongly associated with changes in rainfall (P = 0.05 to P = 0.001). The overall rainfall amount is critical in determining yields but, equally, higher future temperatures can contribute to reducing crop productivity primarily due to advanced crop phenology. For example, for wheat cropping at Hamilton (a higher rainfall site), there is a significant advancement in median flowering date for 2030, 2060, and 2090 of 10, 18, and 29 days respectively with a significant 0.50% grain yield changes for each percentage change in rainfall compared to significant 0.90% grain yield changes in Cunderdin (a lower rainfall sites). At all sites except Hamilton, the change in crop grain yield is significantly correlated (P=0.001) with the percentage change in the future rainfall and the impact increased progressively from higher rainfall to lower rainfall sites. However, the magnitude of the change in crop phenology and yield were not significantly different between soil types. These results help to define regions of concern and their relative importance in the coming years. In this future climate the negative consequences for crop yields and advancement of phenology relative to baseline are not uniform across crops and locations. Of the crops studied-wheat, barley, lupin, canola and field pea-field pea is the most sensitive to the projected future climate changes, and the ensemble median changes in field pea yield range from a decrease of 12% to a decrease of 45%, depending on location. These results highlight the importance of research and policy to support strategies for adapting to climate change, such as advances in agronomy, soil moisture conservation, seasonal climate forecasting and breeding new crop varieties.

In dryland cotton cropping systems, the main weeds and effectiveness of management practices were identified, and the economic impact of weeds was estimated using information collected in a postal and a field survey of Southern Queensland and northern New South Wales. Forty-eight completed questionnaires were returned, and 32 paddocks were monitored in early and late summer for weed species and density. The main problem weeds were bladder ketmia (Hibiscus trionum), common sowthistle (Sonchus oleraceus), barnyard grasses (Echinochloa spp.), liverseed grass (Urochloa panicoides) and black bindweed (Fallopia convolvulus), but the relative importance of these differed with crops, fallows and crop rotations. The weed flora was diverse with 54 genera identified in the field survey. Control of weed growth in rotational crops and fallows depended largely on herbicides, particularly glyphosate in fallow and atrazine in sorghum, although effective control was not consistently achieved. Weed control in dryland cotton involved numerous combinations of selective herbicides, several non-selective herbicides, inter-row cultivation and some manual chipping. Despite this, residual weeds were found at 38–59% of initial densities in about 3-quarters of the survey paddocks. The on-farm financial costs of weeds ranged from $148 to 224/ha.year depending on the rotation, resulting in an estimated annual economic cost of $19.6 million. The approach of managing weed populations across the whole cropping system needs wider adoption to reduce the weed pressure in dryland cotton and the economic impact of weeds in the long term. Strategies that optimise herbicide performance and minimise return of weed seed to the soil are needed. Data from the surveys provide direction for research to improve weed management in this cropping system. The economic framework provides a valuable measure of evaluating likely future returns from technologies or weed management improvements.

Productivity of grain crops grown under dryland conditions in north-eastern Australia depends on efficient use of rainfall and available soil moisture accumulated in the period preceding sowing. However, adverse subsoil conditions including high salinity, sodicity, nutrient imbalances, acidity, alkalinity, and high concentrations of chloride (Cl) and sodium (Na) in many soils of the region restrict ability of crop roots to access this stored water and nutrients. Planning for sustainable cropping systems requires identification of the most limiting constraint and understanding its interaction with other biophysical factors. We found that the primary effect of complex and variable combinations of subsoil constraints was to increase the crop lower limit (CLL), thereby reducing plant available water. Among chemical subsoil constraints, subsoil Cl concentration was a more effective indicator of reduced water extraction and reduced grain yields than either salinity or sodicity (ESP). Yield penalty due to high subsoil Cl was seasonally variable, with more in-crop rainfall (ICR) resulting in less negative impact. A conceptual model to determine realistic yield potential in the presence of subsoil Cl was developed from a significant positive linear relationship between CLL and subsoil Cl: Since grid sampling of soil to identify distribution of subsoil Cl, both spatially across landscape and within soil profile, is time-consuming and expensive, we found that electromagnetic induction, coupled with yield mapping and remote sensing of vegetation offers potential to rapidly identify possible subsoil Cl at paddock or farm scale. Plant species and cultivars were evaluated for their adaptations to subsoil Cl. Among winter crops, barley and triticale, followed by bread wheat, were more tolerant of high subsoil Cl concentrations than durum wheat. Chickpea and field pea showed a large decrease in yield with increasing subsoil Cl concentrations and were most sensitive of the crops tested. Cultivars of different winter crops showed minor differences in sensitivity to increasing subsoil Cl concentrations. Water extraction potential of oilseed crops was less affected than cereals with increasing levels of subsoil Cl concentrations. Among summer crops, water extraction potential of millet, mungbean, and sesame appears to be more sensitive to subsoil Cl than that of sorghum and maize; however, the differences were significant only to 0.7 m. Among pasture legumes, lucerne was more tolerant to high subsoil Cl concentrations than the others studied. Surface applied gypsum significantly improved wheat grain yield on soils with ESP >6 in surface soil (0–0.10 m). Subsurface applied gypsum at 0.20–0.30 m depth did not affect grain yield in the first year of application; however, there was a significant increase in grain yield in following years. Better subsoil P and Zn partially alleviated negative impact of high subsoil Cl. Potential savings from improved N fertilisation decisions for paddocks with high subsoil Cl are estimated at ~$AU10 million per annum.

Two issues prompted this paper. The first was the measured soil organic carbon decline in fertile northern Australian soils under continual cropping using traditional management practices. We wanted to see whether it was theoretically possible to maintain or improve soil organic carbon concentrations with modern management recommendations. The second was the debate about use of sustainability indicators for on-farm management, so we looked at soil organic carbon as a potential indicator of soil health and investigated whether it was useful in making on-farm crop decisions. The analytical results indicated first that theoretically the observed decline in soil organic carbon concentrations in some northern cracking clay soils can be halted and reversed under continuous cropping sequences by using best practice management. Second, the results and associated discussion give some support to the use of soil organic carbon as a sustainability indicator for soil health. There was a consistent correlation between crop input decisions (fertilisation, stubble management, tillage), outputs (yield and profits) and outcomes (change in soil organic carbon content) in the short and longer term. And this relationship depended to some extent on whether the existing soil organic carbon status was low, medium or high. A stock dynamics relationship is one where the change in a stock (such as soil organic carbon) through time is related not only to the management decisions made and other random influences (such as climatic effects), but also to the concentration or level of the stock itself in a previous time period. Against such a requirement, soil organic carbon was found to be a reasonable measure. However, the inaccuracy in measuring soil organic carbon in the paddock mitigates the potential benefit shown in this analysis of using soil organic carbon as a sustainability indicator.These results are based on a simulation model (APSIM) calibrated for a cracking clay (Vertosol) soil typical of much of the intensively-cropped slopes and plains region of northern New South Wales and southern Queensland, and need to be interpreted in this light. There are large areas of such soils in north-western New South Wales; however, many of these experience lower rainfalls and plant-available soil water capacities than in this case, and the importance of these characteristics must also be considered.

Mobile phone applications (apps) designed to assist smallholder farmers improve decision‐making have been revolutionizing the agriculture sector. These apps offer solutions to farmer information needs by providing weather information, crop market trends, pest and disease damage identification, and advice on pesticide and fertilizer use. They also facilitate interaction with fellow farmers, extension workers and other stakeholders in the value chain who are interested in information exchange. Much previous research has investigated the contribution of mobile apps to agricultural production. This study explored the agricultural mobile apps available in Myanmar, analyzed factors affecting their use and assessed the potential for farm‐based decision support. Our findings indicate that when introducing mobile‐based tools, focus should be given to younger, more educated farmers growing more specialized crops. The main constraints to adopt agricultural apps are lack of access to smartphone and/or internet (63%) and lack of digital knowledge (20%). However, smallholder farmers in Myanmar were optimistic and positive toward agricultural apps for effective utilization. We also found that majority of the surveyed farmers were familiar with information received through Facebook groups. Incorporating useful information and functions from an agricultural mobile app to a Facebook Page could have a more useful and sustainable impact.